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of 194
pro vyhledávání: '"Chien, Jen‑Tzung"'
Student mental health is a sensitive issue that necessitates special attention. A primary concern is the student-to-counselor ratio, which surpasses the recommended standard of 250:1 in most universities. This imbalance results in extended waiting pe
Externí odkaz:
http://arxiv.org/abs/2411.00604
The prevalence of the powerful multilingual models, such as Whisper, has significantly advanced the researches on speech recognition. However, these models often struggle with handling the code-switching setting, which is essential in multilingual sp
Externí odkaz:
http://arxiv.org/abs/2312.08856
Contrastive speaker embedding assumes that the contrast between the positive and negative pairs of speech segments is attributed to speaker identity only. However, this assumption is incorrect because speech signals contain not only speaker identity
Externí odkaz:
http://arxiv.org/abs/2309.13253
Contrastive self-supervised learning (CSL) for speaker verification (SV) has drawn increasing interest recently due to its ability to exploit unlabeled data. Performing data augmentation on raw waveforms, such as adding noise or reverberation, plays
Externí odkaz:
http://arxiv.org/abs/2309.04265
This paper presents a parameter-efficient learning (PEL) to develop a low-resource accent adaptation for text-to-speech (TTS). A resource-efficient adaptation from a frozen pre-trained TTS model is developed by using only 1.2\% to 0.8\% of original t
Externí odkaz:
http://arxiv.org/abs/2305.11320
Akademický článek
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Autor:
Xie, Jiyang, Ma, Zhanyu, Zhang, Guoqiang, Xue, Jing-Hao, Chien, Jen-Tzung, Lin, Zhiqing, Guo, Jun
A Bayesian approach termed BAyesian Least Squares Optimization with Nonnegative L1-norm constraint (BALSON) is proposed. The error distribution of data fitting is described by Gaussian likelihood. The parameter distribution is assumed to be a Dirichl
Externí odkaz:
http://arxiv.org/abs/1807.02795
Autor:
Chien, Jen-Tzung, Chen, Ming-Yen, Lee, Ching-hsien, Xue, Jing-Hao, Wang, Jia-Ching, Wang, Hsin-Min, Peng, Wen-Hsiao, Yeh, Chia-Hung
Publikováno v:
APSIPA Transactions on Signal & Information Processing; 2024, Vol. 13 Issue 5, p1-31, 31p
